"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a				=logging.get_logger(__name__)

a				={
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Dict    				=			'''data2vec-vision'''

				def __init__( self      ,					_lowerCamelCase=768      ,					_lowerCamelCase=12      ,					_lowerCamelCase=12      ,					_lowerCamelCase=3072      ,					_lowerCamelCase="gelu"      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1E-12      ,					_lowerCamelCase=224      ,					_lowerCamelCase=16      ,					_lowerCamelCase=3      ,					_lowerCamelCase=False      ,					_lowerCamelCase=False      ,					_lowerCamelCase=False      ,					_lowerCamelCase=False      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=True      ,					_lowerCamelCase=[3, 5, 7, 11]      ,					_lowerCamelCase=[1, 2, 3, 6]      ,					_lowerCamelCase=True      ,					_lowerCamelCase=0.4      ,					_lowerCamelCase=256      ,					_lowerCamelCase=1      ,					_lowerCamelCase=False      ,					_lowerCamelCase=255      ,					**_lowerCamelCase      ,					):
										super().__init__(**_lowerCamelCase )

										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =hidden_act
										lowerCamelCase__   =hidden_dropout_prob
										lowerCamelCase__   =attention_probs_dropout_prob
										lowerCamelCase__   =initializer_range
										lowerCamelCase__   =layer_norm_eps

										lowerCamelCase__   =image_size
										lowerCamelCase__   =patch_size
										lowerCamelCase__   =num_channels
										lowerCamelCase__   =use_mask_token
										lowerCamelCase__   =use_absolute_position_embeddings
										lowerCamelCase__   =use_relative_position_bias
										lowerCamelCase__   =use_shared_relative_position_bias
										lowerCamelCase__   =layer_scale_init_value
										lowerCamelCase__   =drop_path_rate
										lowerCamelCase__   =use_mean_pooling
										# decode head attributes (semantic segmentation)
										lowerCamelCase__   =out_indices
										lowerCamelCase__   =pool_scales
										# auxiliary head attributes (semantic segmentation)
										lowerCamelCase__   =use_auxiliary_head
										lowerCamelCase__   =auxiliary_loss_weight
										lowerCamelCase__   =auxiliary_channels
										lowerCamelCase__   =auxiliary_num_convs
										lowerCamelCase__   =auxiliary_concat_input
										lowerCamelCase__   =semantic_loss_ignore_index






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Tuple    				=			version.parse('''1.11'''      )

				@property
				def 						_a      ( self ):
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ] )






				@property
				def 						_a      ( self ):
										return 1E-4



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[Any]    				=			'''Wav2Vec2FeatureExtractor'''
				A__ :  Any    				=			'''AutoTokenizer'''

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										try:
																return super().from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
										except OSError:
																warnings.warn(
																    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																    " include a `tokenizer_class` attribute is deprecated and will be "
																    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																    " attribute to either your `config.json` or `tokenizer_config.json` "
																    "file to suppress this warning: "      ,					_lowerCamelCase      ,					)

																lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
																lowerCamelCase__   =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																return cls(feature_extractor=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )

				def __call__( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowerCamelCase      ,					**_lowerCamelCase )

										if "raw_speech" in kwargs:
																warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
																lowerCamelCase__   =kwargs.pop("raw_speech" )
										else:
																lowerCamelCase__   =kwargs.pop("audio"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("sampling_rate"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("text"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if audio is None and text is None:
																raise ValueError("You need to specify either an `audio` or `text` input to process." )

										if audio is not None:
																lowerCamelCase__   =self.feature_extractor(_lowerCamelCase      ,					*_lowerCamelCase      ,					sampling_rate=_lowerCamelCase      ,					**_lowerCamelCase )
										if text is not None:
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					**_lowerCamelCase )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																lowerCamelCase__   =encodings["input_ids"]
																return inputs

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor.pad(*_lowerCamelCase      ,					**_lowerCamelCase )

										lowerCamelCase__   =kwargs.pop("input_features"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("labels"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if input_features is not None:
																lowerCamelCase__   =self.feature_extractor.pad(_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase )
										if labels is not None:
																lowerCamelCase__   =self.tokenizer.pad(_lowerCamelCase      ,					**_lowerCamelCase )

										if labels is None:
																return input_features
										elif input_features is None:
																return labels
										else:
																lowerCamelCase__   =labels["input_ids"]
																return input_features

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )






				@contextmanager
				def 						_a      ( self ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call." )
										lowerCamelCase__   =True
										lowerCamelCase__   =self.tokenizer
										yield
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False


